Intelligent Language Processing for Understanding Financial Text

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This Innovation Fellowship addresses the theme of Intelligent Language Processing in support of concrete needs for a specific business partner - as well as generating positive impact for UK industry more broadly. The fellowship is oriented to the issue of language as a 'hard problem' within the sphere of robotics and artificial intelligence (AI), these being a technological challenge identified by the UK Industrial Strategy. The 'hard problem' is that the understanding of language is rooted not merely in the words of the text or speech itself but also in the context - including world knowledge possessed by human communicators but largely lacking in AI. Language understanding is thus, from the perspective of AI, a higher level cognitive task of the kind most challenging for automated systems. Consider, for instance, the word ''bank''. To properly handle the use of such a word in a text (written or spoken), it is first necessary to identify its grammatical function. This is relatively straightforward (''I bank at HSBC'' - verb; vs. ''HSBC is a global bank'' - noun). Less straightforward is distinguishing meanings - a financial ''bank'' versus a river ''bank'', for instance. Even more difficult for AI is distinguishing cases where the same grammatical form and same basic meaning must be interpreted differently due to subtleties of meaning/implication: consider ''I put my money in the bank'' (as a customer) / ''I made my money in the bank'' (as an employee) / ''I lost my money in the bank'' (a bank as a physical location rather than a corporate entity).Multiple research fields have developed methods that address this issue to some extent, allowing computer-assisted language understanding. These techniques are variously referred to corpus-based methods, or as natural language processing. They involve applying digital technology either (i) to train computers to accomplish analytic tasks via machine learning based on very large language datasets; or (ii) to down-sample and sift such large datasets to guide a human analyst to the details they seek. These are all varieties of Intelligent Language Processing: that is, using detailed knowledge of language, derived via machine-driven analysis of textual 'big data', to drive enhanced language-based AI for practical exploitation of yet further such data. The key challenge this Fellowship addresses is the need to make these cutting-edge AI techniques transformative for business and industry, delivering not only for the overall Industrial Strategy, but also for the business needs of our primary private sector partner: KPMG and additional secondary partners in the regulatory sector. The method overall is to apply Intelligent Language Processing to the task of understanding types of text produced in corporate financial contexts - Financial Text. TWO major problems will be addressed in support of business impact and knowledge exchange: First - the extraction of numerical information from textual documents produced by companies. (What can the narratives a firm publishes tell us about their financial situation, in actual pounds)? Second - working out how to compare financial text between different channels. (How do companies narrate their status differently in documents directed to different audiences, and what does that tell us about their actual status? )These problems will be addressed through THREE research work projects in this Fellowship. (1) Linking together different texts for different audiences, and understanding tone and positivity/negativity. (2) Looking internationally - how do corporate documents vary around the world, in different legal / cultural context. (3) Trust / influence - how do companies use language to presuade and establish trust in their communications to different audiences?